Toward a genome-based treatment landscape for renal cell carcinoma

Prognostic and predictive biomarkers for immunotherapy in advanced renal cell carcinoma

The therapeutic algorithm of renal cell carcinoma has been revolutionized by the approval of immunotherapy agents by regulatory agencies. However, objective and durable responses are still not observed in a large number of patients, and prognostic and predictive biomarkers for immunotherapy response are urgently needed. Prognostic models used in clinical practice are based on clinical and laboratory factors (such as hypercalcaemia, neutrophil count or Karnofsky Performance Status), but, with progress in molecular biology and genome sequencing techniques, new renal cell carcinoma molecular features that might improve disease course and outcomes prediction have been highlighted. An implementation of current models is needed to improve the accuracy of prognosis in the immuno-oncology era. Moreover, several potential biomarkers are currently under evaluation, but effective markers to select patients who might benefit from immunotherapy and to guide therapeutic strategies are still far from validation.

Cuproptosis-Related LncRNA-Based Prediction of the Prognosis and Immunotherapy Response in Papillary Renal Cell Carcinoma

Cuproptosis, a new cell death pattern, is promising as an intervention target to treat tumors. Abnormal long non-coding RNA (lncRNA) expression is closely associated with the occurrence and development of papillary renal cell carcinoma (pRCC). However, cuproptosis-related lncRNAs (CRLs) remain largely unknown as prognostic markers for pRCC. We aimed to forecast the prognosis of pRCC patients by constructing models according to CRLs and to examine the correlation between the signatures and the inflammatory microenvironment. From the Cancer Genome Atlas (TCGA), RNA sequencing, genomic mutations and clinical data of TCGA-KIRP (Kidney renal papillary cell carcinoma) were analyzed. Randomly selected pRCC patients were allotted to the training and testing sets. To determine the independent prognostic impact of the training characteristic, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized, together with univariate and multivariate Cox regression models. Further validation was performed in the testing and whole cohorts. External datasets were utilized to verify the prognostic value of CRLs as well. The CRLs prognostic features in pRCC were established based on the five CRLs (AC244033.2, LINC00886, AP000866.1, MRPS9-AS1 and CKMT2-AS1). The utility of CRLs was evaluated and validated in training, testing and all sets on the basis of the Kaplan-Meier (KM) survival analysis. The risk score could be a robust prognostic factor to forecast clinical outcomes for pRCC patients by the LASSO algorithm and univariate and multivariate Cox regression. Analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) data demonstrated that differentially expressed genes (DEGs) are primarily important for immune responses and the PI3K-Akt pathway. Arachidonic acid metabolism was enriched in the high-risk set by Gene Set Enrichment Analysis (GSEA). In addition, Tumor Immune Dysfunction and Exclusion (TIDE) analysis suggested that there was a high risk of immune escape in the high-risk cohort. The immune functions of the low- and high-risk sets differed significantly based on immune microenvironment analysis. Finally, four drugs were screened with a higher sensitivity to the high-risk set. Taken together, a novel model according to five CRLs was set up to forecast the prognosis of pRCC patients, which provides a potential strategy to treat pRCC by a combination of cuproptosis and immunotherapy.

Cuproptosis patterns in papillary renal cell carcinoma are characterized by distinct tumor microenvironment infiltration landscapes

Cuproptosis is a novel kind of programmed cell death that has been linked to tumor development, prognosis, and responsiveness to therapy. Nevertheless, the precise function of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) remains unknown. We characterized the genetic and transcriptional changes of CRGs in papillary renal cell carcinoma (PRCC) samples and analyzed the expression patterns in two separate cohorts. We observed that two unique cuproptosis-related subgroups and three separate gene subgroups were connected with clinicopathological, prognostic, and TME features of patients. Then, a risk score for predicting overall survival (OS) was created and validated in patients with PRCC. To make the risk score more clinically useful, we created a nomogram that was very accurate. A lower risk score, which was associated with higher tumor mutation burden, and immune activity, suggested a better prognosis for OS. Additionally, the risk score was shown to be substantially linked with the drug’s susceptibility to chemotherapeutic agents. Our extensive research of CRGs in PRCC identified possible roles for them in the TME, clinicopathological features, and overall survival. These findings may help advance our knowledge of CRGs in PRCC and pave the way for improved prognosis and the creation of more effective immunotherapy therapies.

Identification of immunization-related new prognostic biomarkers for papillary renal cell carcinoma by integrated bioinformatics analysis

Background

Despite papillary renal cell carcinoma (pRCC) being the second most common type of kidney cancer, the underlying molecular mechanism remains unclear. Targeted therapies in the past have not been successful because of the lack of a clear understanding of the molecular mechanism. Hence, exploring the underlying mechanisms and seeking novel biomarkers for pursuing a precise prognostic biomarker and appropriate therapies are critical.

Material and methods

In our research, the differentially expressed genes (DEGs) were screened from the TCGA and GEO databases, and a total of 149 upregulated and 285 downregulated genes were sorted. This was followed by construction of functional enrichment and protein–protein interaction (PPI) network, and then the top 15 DEGs were selected for further analysis. The P4HB gene was chosen as our target gene by repetitively validating multiple datasets, and higher levels of P4HB expression predicted lower overall survival (OS) in patients with pRCC.

Results

We found that P4HB not only connects with immune cell infiltration and co-expression with PD-1, PD-L2, and CTLA-4, but also has a strong connection with the newly discovered hot gene, TOX.

Conclusion

We speculate that P4HB is a novel gene involved in the progression of pRCC through immunomodulation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01092-w.

Immunotherapy and Radiation Therapy in Renal Cell Carcinoma

BACKGROUND

The management of renal cell carcinoma is rapidly evolving and immunotherapy, mostly consisting of immune checkpoint inhibitors, is revolutionizing the treatment scenario of metastatic patients. Novel fractionation schedules of radiotherapy, consisting of high doses in few fractions, can overcome the radioresistance of this tumor. Localized radiotherapy is associated with a systemic effect, known as the abscopal effect. This effect mediated by the immune system can be enhanced associating radiotherapy with immunotherapy.

OBJECTIVE

In this review, we explore the role of radiotherapy and immunotherapy in RCC, the rationale of combining these strategies and the on-going clinical trials investigating combinations of these two treatment modalities.

CONCLUSION

Combining immunotherapy and radiotherapy has a strong rationale and pre-clinical studies support their association because it can overcome the immunosuppression of the tumor microenvironment and increase the anti-tumor immune response. More clinical evidence, deriving from onclinical trials, are needed to prove the efficacy and safety of these treatments combined.

Tumor Growth Rate Decline despite Progressive Disease May Predict Improved Nivolumab Treatment Outcome in mRCC: When RECIST Is Not Enough

Simple Summary

The treatment scenario of metastatic renal cell carcinoma has drastically changed in recent years, with the advent of immunotherapy. Since 2015 immune-checkpoint inhibitors, either alone or in combination with other compounds, are constantly enriching the treatment scenario, with a drastic change of patients’ outcomes. The benefit from immunotherapy is difficult to capture with the currently available assessment radiological criteria. Often, with rhe use of immunotherapy, we can observe atypical patterns of response, such as hyperprogression or pseudoprogression. Pseudoprogression consists of an initial increase in tumor burden followed by a response to therapy, while hyperprogression is defined as a tumor growth rate that was at least 2-fold greater in patients with disease progression during immunotherapy. We performed a retrospective monocentric study to explore the impact of tumor growth rate change after immunotherapy administration as second or later line of treatment in patients with metastatic renal cell carcinoma.

Abstract

Treatment response is usually assessed by the response evaluation criteria in solid tumors (RECIST). These criteria may not be adequate to evaluate the response to immunotherapy, considering the peculiar patterns of response reported with this therapy. With the advent of immunotherapy these criteria have been modified to include the evaluation of the peculiar responses seen with this type of therapy (iRECIST criteria), including pseudoprogressions and hyperprogressions. Tumor growth rate (TGR) is a dynamic evaluation that takes into account the kinetics of response to treatment and may help catch the real efficacy of an immunotherapy approach. We performed a retrospective monocentric study to explore the impact of TGR change after nivolumab administration as the second or later line of treatment in patients with metastatic renal cell carcinoma (RCC). We evaluated 27 patients, divided into three categories: Disease control (DC) if there was no PD; lower velocity PD (LvPD) if disease progressed but the TGR at second assessment (TGR2) was lower than the TGR at first assessment (TGR1); higher velocity PD (HvPD) if TGR2 was higher than TGR1. The median OS for the DC group was 11.0 months (95% CI 5.0–17.0) (reference) vs. (not reached) NR (95% CI NR-NR) for LvPD (HR 0.27; 95% CI 0.06–1.30; p 0.102) vs. NR (95% CI NR–NR) for HvPD (HR 0.23; 95% CI 0.06–0.88; p 0.032). There was no difference between LvPD and DC (HR 1.21; 95% CI 0.20–7.28; p 0.838). In patients with metastatic RCC, the second or later line of nivolumab treatment may lead to a deceleration in TGR resulting in an improved survival outcome similar to that observed in patients experiencing tumor regression. In this subgroup, especially in the presence of a clinical benefit, continuing the treatment beyond progression can be recommended.

Genome-Wide Transcriptomic Analysis of Non-Tumorigenic Tissues Reveals Aging-Related Prognostic Markers and Drug Targets in Renal Cell Carcinoma

The relationship between expression of aging-related genes in normal tissues and cancer patient survival has not been assessed. We developed a genome-wide transcriptomic analysis approach for normal tissues adjacent to the tumor to identify aging-related transcripts associated with survival outcome, and applied it to 12 cancer types. As a result, five aging-related genes (DUSP22, MAPK14, MAPKAPK3, STAT1, and VCP) in normal tissues were found to be significantly associated with a worse survival outcome in patients with renal cell carcinoma (RCC). This computational approach was investigated using nontumorigenic immune cells purified from young and aged mice. Aged immune cells showed upregulated expression of all five aging-related genes and promoted RCC invasion compared to young immune cells. Further studies revealed DUSP22 as a regulator and druggable target of metastasis. DUSP22 gene knockdown reduced RCC invasion and the small molecule inhibitor BML-260 prevented RCC dissemination in a tumor/immune cell xenograft model. Overall, these results demonstrate that deciphering the relationship between aging-related gene expression in normal tissues and cancer patient survival can provide new prognostic markers, regulators of tumorigenesis and novel targets for drug development.

The Molecular Characteristics of Non-Clear Cell Renal Cell Carcinoma: What's the Story Morning Glory?

Non-clear cell renal cell carcinomas are a miscellaneous group of tumors that include different histological subtypes, each one characterized by peculiarity in terms of genetic alteration, clinical behavior, prognosis, and treatment response. Because of their low incidence and poor enrollment in clinical trials, alongside their heterogeneity, additional efforts are required to better unveil the pathogenetic mechanisms and, consequently, to improve the treatment algorithm. Nowadays, tyrosine kinase inhibitors, mTOR and MET inhibitors, and even cisplatin-based chemotherapy and immunotherapy are potential weapons that are still under evaluation in this setting. Various biomarkers have been evaluated for detecting progression and monitoring renal cell carcinoma, but more studies are necessary to improve this field. In this review, we provide an overview on the molecular characteristics of this group of tumors and the recently published trials, giving an insight into what might become the future therapeutic standard in this complex world of non-clear cell kidney cancers.

Broad spectrum mutational analysis of chromophobe renal cell carcinoma using next-generation sequencing

BACKGROUND

Chromophobe renal cell carcinoma (ChRCC) is a rare subtype of non-clear cell renal cell carcinoma. Due to its rarity, its molecular characterization as well as therapeutic targets are still not fully understood.

METHODS

We performed a next-generation sequencing analysis using the platform Ion PGM System on 20 retrospectively collected ChRCC cases with the aim of identify molecular biomarkers with potential prognostic value or that could have therapeutic implications.

RESULTS

We identified mutation onTP53, SMARCB1, RB1 and JAK3. The most frequently altered gene was TP53 (6/20, 30 % of cases). SMARCB1 mutation was found in 3 (15 %) patients and in all cases the mutational variant was p.T72 K, with known pathogenenic meaning. One (5%) patient presented a pathogenetic mutation of RB1. JAK3 was mutated in 1 (5%) patient and this mutation resulted to have uncertain pathogenetic significance.

CONCLUSION

ChRCC is a rare disease still not fully molecularly characterized. Next-generation sequencing analysis could be useful to identify potential mutation with prognostic value or that could be potential therapeutic targets.

Individualizing Systemic Therapies in First Line Treatment and beyond for Advanced Renal Cell Carcinoma

Therapeutic options for treating advanced renal cell cancer (RCC) are rapidly evolving. Vascular endothelial growth factor (VEGF)-directed therapy, predominantly VEGF receptor (VEGFr) tyrosine kinase inhibitors (TKIs) had been the most effective first line treatment since 2005 irrespective of International Metastatic RCC Database Consortium (IMDC) risk stratification. However, immune checkpoint inhibitors (ICI) have recently changed the treatment paradigm for advanced RCC particularly as the first-line systemic treatment modality. The combination of Ipilimumab and Nivolumab provides better disease control and long-term outcomes compared with the anti-VEGFr TKI Sunitinib for IMDC intermediate- to poor-risk patients and we now have the option of using ICI with TKI upfront for all IMDC risk groups. This poses a challenge for physicians, both to select the most suitable first line regimen and the most suitable subsequent therapy given the lack of data about sequencing in this setting. This treatment landscape is expected to become more complex with the emerging treatment options. Moreover, these therapeutic options cannot be generalized as significant variability exists between individual's disease biologies and their physiologies for handling treatment adverse effects. Notable efforts are being made to identify promising predictive biomarkers ranging from neo-antigen load to gene expression profiling. These biomarkers need prospective validation to justify their utility in clinical practice and in treatment decision making. This review article discusses various clinicopathological characteristics that should be carefully evaluated to help select appropriate treatment and discusses the current status of biomarker-based selection.

Prognostic and predictive factors to nivolumab in patients with metastatic renal cell carcinoma: a single center study

Renal cell carcinoma (RCC) scenario has radically changed with the advent of immunotherapy; in this setting, the identification of predictive and prognostic factors represents an urgent clinical need to evaluate which patients are the best candidate for an immunotherapy approach. The aim of our study was to analyze the association between nivolumab in pretreated patients with metastatic RCC and clinicopathological features, metastatic sites, and clinical outcomes. A total of 37 patients treated between January 2017 and April 2020 in our institution were retrospectively evaluated. All patients received nivolumab as second- or later-line of therapy after progression on previous tyrosine kinase inhibitors. The primary outcomes were overall survival (OS) from immunotherapy start and OS from first-line start. Univariate analysis was performed through the log-rank test and a Cox regression proportional hazards model was employed in multivariable analysis. Of the 12 variables analyzed, 4 were significantly associated with prognoses at multivariate analysis. Cox proportional hazard ratio models confirmed that International Metastatic Renal-Cell Carcinoma Database Consortium (IMDC) risk group, liver metastases at diagnosis, and central nervous system (CNS) metastases at diagnosis were associated with worse OS with an estimated hazard ratio of 4.76 [95% confidence interval (CI), 2.05-19.8] for liver metastases and 2.27 (95% CI, 1.13-28.9) for CNS metastases. Pancreatic metastases at diagnosis were correlated to a better prognosis with an estimated hazard ratio of 0.15 (95% CI, 0.02-0.38). IMDC risk group, liver metastases at diagnosis, and CNS metastases at diagnosis may identify a population of patients treated with immunotherapy in second- or later-line associated with worse prognosis.

Cancer predictive studies

The identification of individual or clusters of predictive genetic alterations might help in defining the outcome of cancer treatment, allowing for the stratification of patients into distinct cohorts for selective therapeutic protocols. Neuroblastoma (NB) is the most common extracranial childhood tumour, clinically defined in five distinct stages (1–4 & 4S), where stages 3–4 define chemotherapy-resistant, highly aggressive disease phases. NB is a model for geneticists and molecular biologists to classify genetic abnormalities and identify causative disease genes. Despite highly intensive basic research, improvements on clinical outcome have been predominantly observed for less aggressive cancers, that is stages 1,2 and 4S. Therefore, stages 3–4 NB are still complicated at the therapeutic level and require more intense fundamental research. Using neuroblastoma as a model system, here we herein outline how cancer prediction studies can help at steering preclinical and clinical research toward the identification and exploitation of specific genetic landscape. This might result in maximising the therapeutic success and minimizing harmful effects in cancer patients.

Immunotherapy in renal cell carcinoma from poverty to the spoiled of choice

Management of metastatic renal cell carcinoma has been enriched by the advent of new therapeutic compounds. The approval of new combination strategies between targeted agents and immune-checkpoint inhibitors as well as the administration of combinations between immune-checkpoint inhibitors has clearly demonstrated significant improvement toward patients' prognosis and other clinical outcomes. Due to the availability of different treatments, the choice between them may be a difficult issue in our clinical practice. We have summarized current knowledge about available treatments focusing on criteria, which may help clinicians to make decisions.